Towards Autism Subtype Detection Through Identification of Discriminatory Factors Using Machine Learning.

Autism spectrum disorder (ASD) is a neuro-developmental disease that has a lifetime impact on a person’s ability to interact and communicate with others. Early discovery of autism can assist to prepare a plan for suitable therapy and reduce its impact on patients at an appropriate time. The aim of this work is to propose a machine learning model which generates autism subtypes and identifies discriminatory factors among them. In this work, we use Quantitative Checklist for Autism in Toddlers-10 (Q-CHAT-10) of toddler and Autism Spectrum Quotient-10 (AQ-10) datasets of child, adolescent, and adult screening datasets respectively. Then, only autism records are merged and implemented k-means algorithm to extract various autism subtypes. According to Silhoutte score, we select the best autism dataset and balance its subtypes using random oversampling (ROS) and synthetic minority oversampling technique for numeric and categorical values (SMOTENC). Afterwards, various classifiers are employed into both primary dataset and its balanced subtypes. In this work, logistic regression shows the highest result for primary dataset. Also, it achieves the greatest results for ROS and SMOTENC datasets. Hence, shapely adaptive explanation (SHAP) technique is used to rank features and scrutinized discriminatory factors of these autism subtypes.